Development of a Knowledge Graph-Based Model for Recommending MOOCs to Supplement University Educational Programs in Line With Employer Requirements - Valiya Ramazanova; Madina Sambetbayeva; Sandugash Serikbayeva; Zhanna Sadirmekova; Aigerim Yerimbetova (IEEE Access)
The modern labor market demands that educational institutions prepare specialists capable of effectively responding to rapidly changing professional standards and technologies. In this regard, the use of innovative approaches to adapt educational programs has become a key factor. This study is dedicated to developing a methodology for using heterogeneous knowledge graphs to create a recommendation system aimed at bridging the gap between existing educational courses and the dynamically changing requirements of the labor market. The central element of the study is the use of knowledge graphs to aggregate and analyze diverse data on skills, job vacancies, and educational courses. Knowledge graphs not only structure large volumes of information but also visualize complex connections between various educational modules and professional requirements. This approach fosters a deeper understanding of how educational programs can be adjusted to match the market specifics. An important aspect of the study is the application of multilingual semantic similarity algorithms to analyze and match skills. These algorithms play a key role in determining the degree of correspondence between the skills listed in educational programs and courses, and those required for specific job vacancies. The use of natural language processing techniques allows not only capturing explicit keyword matches, but also recognizing deep semantic connections, which is an integral part of accurate matching in educational and professional domains. The results of the study demonstrate that the proposed methodology can effectively analyze the multilingual relationships between educational and professional skills, which improves personalized courses and job recommendations. Our study contributes to the literature by proposing a new methodology for building recommendations that improves the accuracy of personalized educational and career recommendations, and facilitates the adaptation of educational programs to dynamic changes in the labor market.